Turbulent Events Research Articles

Unveiling gas kinematics and stellar populations in H ii regions inside the low-metallicity dwarf nearby galaxy SDSSJ0859+3923 with MEGARA at the GTC

Abstract In this study, we present Integral Field Unit observations of the galaxy SDSSJ0859+3923, utilizing the MEGARA instrument on the GTC 10.4m telescope. These observations were conducted in two distinct spectral ranges: 4332 – 5222 Å and 6097 – 7345 Å, with a high resolving power (RFWHM ∼6 000), and spatial resolution of 25 pc, considering the galaxy’s distance of 8.5 Mpc. Our observations have identified five H ii regions, whose precise positions were determined using data from the FWFC3-UVIS/HST archive images, where we also detected the associated blue underlying continuum linked to the ionized knots. A detailed kinematic analysis of these regions revealed low velocity dispersion values (around 10 km s−1) in four H ii regions, indicating a lack of significant turbulent events. In the fifth region we observed a peak in velocity dispersion reaching 40 km s−1, which we interpret as the result of hot star winds and/or a recent type-II supernova explosion. We have conducted a comprehensive spectral analysis of the H ii regions, obtaining emission-line fluxes that enabled us to confirm the oxygen abundance (12 + log(O/H) = 7.41 ± 0.15) and, using popstar models, to constrain the age and mass of the ionizing young clusters.

Scalar mixing and entrainment in an axisymmetric jet subjected to external turbulence

The present study aims to understand the process of turbulent entrainment into a jet, as affected by background turbulence, using scalar statistics. Planar laser-induced fluorescence was employed to capture the orthogonal cross sections of the jet at a fixed downstream station with varying background turbulence intensities and length scales. The conditional scalar profiles revealed that the thickness of the scalar turbulent/turbulent interface is greater than that of the traditional turbulent/non-turbulent interface, and the interfacial thickness is an increasing function of the background turbulence intensity. Although nibbling remains the primary entrainment mechanism in the far field, increased occurrence of concentration “holes” within the interfacial layer in the presence of ambient turbulence suggests a more significant role of large-scale engulfment in the turbulent/turbulent entrainment process (although still below 1% of the total mass flux). Enhanced contribution of the area of detached jet patches (i.e., “islands”) to that of the main jet is hypothesized to be evidence of intense detrainment events in the background turbulence. This can potentially contribute to a reduced net entrainment into the jet, which manifests as less negative values of scalar skewness within the jet core.

The Prospective Application of Machine Learning in Turbulence Forecasting over China

Abstract This study analyzes the spatiotemporal distribution of turbulence in China from 2020 to 2022 using pilot reports. Results reveal a higher frequency of moderate-to-severe turbulence during spring and winter, particularly in January. Spatially, the primary regions of turbulence occurrence are eastern China, Xinjiang Province, Sichuan Province, and the Qiongzhou Strait, with a focus on altitudes at or above 6000 m. Machine learning models, especially random forest and extreme gradient boosting (XGBoost), demonstrate high accuracy in turbulence prediction, notably for high-altitude events. The random forest model shows optimal performance in winter, achieving an area under the curve of 0.92. The study highlights the importance of thermally related diagnostics, indicating a significant presence of convectively induced turbulence in high-altitude turbulence events. This research not only deepens the understanding of turbulence dynamics in the China region but also underscores the potential of machine learning in enhancing turbulence forecasting.

Quantile connectedness between social network sentiment and sustainability index volatility: Evidence from the Moroccan financial market

The goal of this paper is to investigate the connectedness between investor sentiment and volatility on the environmental, social, and governance index (ESG) in Morocco. Therefore, on the basis of an investor sentiment index constructed from the X platform, and using quantile and frequency connectedness approaches, the findings reveal a significant connectedness between sentiment and ESG volatility, particularly during turbulent events. Although sentiment acts most of the time as a net receiver of shocks, notably during the COVID-19 pandemic and geopolitical crises, it sometimes becomes a net sender of shocks. Furthermore, the spillovers between sentiment and ESG volatility are determined mainly by the long-term component, especially during extreme events, implying the persistence of shock transmission due to high uncertainty. The results also illustrate the impact of market conditions on the spillovers between sentiment and ESG volatility. The conclusions of this study provide useful guidance for pro-ESG investors, policymakers, and companies.

Price delay and herding: evidence from the cryptocurrency market

PurposeThe purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.Design/methodology/approachThe paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).FindingsOur findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.Practical implicationsThe study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.Originality/valueThis paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.

Foreign language teaching modes and adaptive methods in emergency education: Evaluation of first-hand experience

Turbulent world events require crisis-sensitive education models that can address new challenges and promote productive learning in extraordinary times. The research aimed to analyze the issue of teaching a foreign language to undergraduate students using intervention techniques in online and offline modes. The researchers suggested the following responsive and adaptive strategies: the creation of adaptive online learning platforms, the development of a multimedia resources bank and the careful selection of innovative teaching tools capable of addressing both psychological and educational needs. The work in groups of both formats was studied through discussion and survey from the point of view of teachers’ perceptions of positive and negative features, difficulties revealed in the education in emergencies, and the productivity of using specially designed adaptive teaching techniques for coping with them. The findings show that in most aspects teachers have a more positive assessment of ‘live’ language teaching in comparison with the online format. They indicate such benefits as more coordinated work, a collaborative and engaging environment, controllability, and greater motivation. The most effective tools used for developing vocabulary, listening skills, reading skills, and speaking skills were revealed. The final testing results in both online and offline groups were analyzed to study the effectiveness of the employed intervention methods. The study demonstrates that the rigorous instructional design is of vital importance for education in an emergency. Special attention should be given to specific adaptive and responsive intervention technologies aimed at interaction and communication enhancement as well as psychological support of students, the choice of which can be determined by the distinctive features of different teaching formats.

SPE-SHAP: Self-paced ensemble with Shapley additive explanation for the analysis of aviation turbulence triggered by wind shear events

Aviation safety is critically challenged by significant turbulence, often triggered by wind shear events, which jeopardize aircraft controllability and may cause structural damage to the fuselage. Current predictive models, primarily based on Pilot Reports (PIREPs), suffer from data imbalance, undermining their reliability and accuracy. This research addresses the need for a robust approach to accurately estimate and classify aviation turbulence events. We propose a novel framework, the Self-Paced Ensemble (SPE), integrated with Shapley Additive Explanation (SHAP) analysis. This framework utilizes tree-based classifiers such as Gradient Boosting Decision Trees (GBDT), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Extra Trees (ET) to enhance predictive accuracy and reliability. The SPE model, particularly when combined with XGBoost, demonstrated superior performance with a recall of 70.16 %, specificity of 74.75 %, G-Mean of 73.67 %, Matthews correlation coefficient (MCC) of 0.352, and area under the Receiver Operating Characteristic (ROC) curve of 0.726. Comparatively, the SPE model with RF achieved a recall of 70.05 %, specificity of 73.89 %, G-Mean of 72.57 %, MCC of 0.347, and AU-ROC of 0.721. Post-hoc Wilcoxon signed-rank tests further validated these results. SHAP analysis identified wind shear altitude, magnitude, and causes as the most influential factors affecting significant aviation turbulence prediction. The SPE-SHAP framework not only significantly improves the prediction of aviation turbulence but also enhances model transparency by elucidating key contributing factors. These insights can inform updates to aircraft operation protocols and enhance flight training, thereby increasing safety during wind shear events.

Extreme statistics and extreme events in dynamical models of turbulence.

We present a study of the intermittent properties of a shell model of turbulence with statistics of ∼10^{7} eddy turn over time, achieved thanks to an implementation on a large-scale parallel GPU factory. This allows us to quantify the inertial range anomalous scaling properties of the velocity fluctuations up to the 24th-order moment. Through a careful assessment of the statistical and systematic uncertainties, we show that none of the phenomenological and theoretical models previously proposed in the literature to predict the anomalous power-law exponents in the inertial range are in agreement with our high-precision numerical measurements. We find that at asymptotically high-order moments, the anomalous exponents tend toward a linear scaling, suggesting that extreme turbulent events are dominated by one leading singularity. We found that systematic corrections to scaling induced by the infrared and ultraviolet (viscous) cutoffs are the main limitations to precision for low-order moments, while high orders are mainly affected by the finite statistical samples.. The high-fidelity numerical results reported in this work offer an ideal benchmark for the development of future theoretical models of intermittency in dynamical systems for either extreme events (high-order moments) or typical fluctuations (low-order moments). For the latter, we show that we achieve a precision in the determination of the inertial range scaling exponents of the order of one part over ten thousand (fifth significant digit), which may be considered a record for out-of-equilibrium fluid-mechanics systems and models.

Statistics of Extreme Events in Integrable Turbulence.

We use the spectral kinetic theory of soliton gas to investigate the likelihood of extreme events in integrable turbulence described by the one-dimensional focusing nonlinear Schrödinger equation (fNLSE). This is done by invoking a stochastic interpretation of the inverse scattering transform for fNLSE and analytically evaluating the kurtosis of the emerging random nonlinear wave field in terms of the spectral density of states of the corresponding soliton gas. We then apply the general result to two fundamental scenarios of the generation of integrable turbulence: (i)the asymptotic development of the spontaneous modulational instability of a plane wave, and (ii)the long-time evolution of strongly nonlinear, partially coherent waves. In both cases, involving the bound state soliton gas dynamics, the analytically obtained values of the kurtosis are in perfect agreement with those inferred from direct numerical simulations of the fNLSE, providing the long-awaited theoretical explanation of the respective rogue wave statistics. Additionally, the evolution of a particular nonbound state gas is considered, providing important insights related to the validity of the so-called virial theorem.

Wave-phase dependence of Reynolds shear stress in the wake of fixed-bottom offshore wind turbine via quadrant analysis

There has been an increase in recognition of the important role that the boundary layer turbulent flow structure has on wake recovery and concomitant wind farm efficiency. Most research thus far has focused on onshore wind farms, in which the ground surface is static. With the expected growth of offshore wind farms, there is increased interest in turbulent flow structures above wavy, moving surfaces and their effects on offshore wind farms. In this study, experiments are performed to analyze the turbulent structure above the waves in the wake of a fixed-bottom model wind farm, with special emphasis on the conditional averaged Reynolds stresses, using a quadrant analysis. Phase-averaged profiles show a correlation between the Reynolds shear stresses and the curvature of the waves. Using a quadrant analysis, Reynolds stress dependence on the wave phase is observed in the phase-dependent vertical position of the turbulence events. This trend is primarily seen in quadrants 1 and 3 (correlated outward and inward interactions). Quantification of the correlation between the Reynolds shear stress events and the surface waves provides insight into the turbulent flow mechanisms that influence wake recovery throughout the wake region and should be taken into consideration in wind turbine operation and placement.

A Consistent Explanation for the Unusual Initial Mass Function and Star Formation Rate in the Central Molecular Zone (CMZ)

We examine various physical processes that may explain the shallow high-mass slope of the initial mass function (IMF), as well as the low star formation rate (SFR) in star-forming molecular clouds (MCs) in the Central Molecular Zone (CMZ). We show that the strong tidal field and shear experienced by the CMZ have opposite effects on the collapse of density fluctuations and cannot explain these properties. Similarly, we show that the intense magnetic field in the CMZ provides a negligible pressure support and, for the high densities at play, should not modify the probability density function of the turbulent gas flow, thus affecting negligibly the IMF. However, we show that, in contrast to the MCs in the Galactic disk, the ones in the CMZ experience only one single episode of turbulence cascade. Indeed, their rather short lifetime, due to their high mean densities, is similar to one typical turbulence crossing time. Consequently, according to the Hennebelle–Chabrier theory of star formation, within this “single turbulence cascade episode,” the cloud experiences one single field of turbulence-induced density fluctuations, leading eventually to gravitationally unstable cores. As shown in Hennebelle & Chabrier (2013), this yields a shallower IMF than usual and leads to the correct observed slope for the CMZ star-forming clouds. Similarly, this single large-scale turbulence event within the cloud lifetime yields a 5–6 times lower SFR than under usual conditions, in agreement with the observed values. Therefore, we suggest that this “single turbulence cascade” scenario can explain both the shallow IMF and low SFR of clouds in the CMZ.

Experimental and numerical gust identification using deep learning models

Identifying gusts and turbulence events is of primary importance for designing future gust load alleviation systems, calculating airframe load, and analysing incidents. Due to the impossibility of their direct measurement, indirect methods are used and ad hoc experiments are necessary to validate the methodology. This paper employs Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) as well as CNN models for in-flight gust identification. Two aeroelastic models, with different levels of fidelity, representative of a civil and commercial aircraft, are used to generate gust responses to train and test the Deep Learning (DL) models. The results highlight the capability of both LSTM-CNN and CNN models in reconstructing gusts across the entire flight envelope of a civil commercial aircraft. The CNN model demonstrated its ability to identify gusts and turbulence when they occur concurrently, similar to real-world scenarios, in a significantly shorter amount of time. Furthermore, its application to wind tunnel gust response measurements, where the inflow has previously been characterised, demonstrated the effectiveness of the proposed methodology for experimental measurements.

A Numerical Study of Clear-Air Turbulence over North China on 6 June 2017

On 6 June 2017, four severe clear-air turbulence (CAT) events were observed over northern China within 3 h. These events mainly occurred at altitudes between 8.1 and 9.5 km. The characteristics and possible mechanisms of the CAT events in the different regions are investigated here using the weather research and forecasting (WRF) model. The simulated wind and temperature fields in a 27 km coarse domain were found to be in good agreement with those of the ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) and the observed soundings of operational radiosondes over northern China. In terms of synoptic features, the region where the turbulence occurred is characterized by a southwest–northeast upper-level jet stream. The upper-level jet stream observed at an altitude of 10.4 km consistently moved eastwards, with a maximum wind speed of 61.7 m/s. Simultaneously, the upper-level front–jet system on the cyclonic shear side of the upper-level jet stream also exhibited an eastward motion. The developed upper-level front–jet system induced significant vertical wind shear (VWS) and tropopause folding in the vicinity of these CAT events. Despite the high stability resulting from tropopause folding, the presence of strong VWS (1.90 × 10−2 s−1–2.55 × 10−2 s−1) led to a low Richardson number (Ri) (0.24–0.88) and caused Kelvin–Helmholtz instability (KHI), which ultimately induced CAT. Although a standard numerical weather forecast resolution of tens of kilometers is adequate to capture turbulence for most CAT events, it is still necessary to use high-resolution numerical simulations (such as 3 km) to calculate more accurate CAT indices (such as Ri) for CAT prediction in some specific cases.

Prandtl number effects on extreme mixing events in forced stratified turbulence

Relatively strongly stratified turbulent flows tend to self-organise into a ‘layered anisotropic stratified turbulence’ (LAST) regime, characterised by relatively deep and well-mixed density ‘layers’ separated by relatively thin ‘interfaces’ of enhanced density gradient. Understanding the associated mixing dynamics is a central problem in geophysical fluid dynamics. It is challenging to study LAST mixing, as it is associated with Reynolds numbers $Re := UL/\nu \gg 1$ and Froude numbers $Fr :=(2{\rm \pi} U)/(L N) \ll 1$ ( $U$ and $L$ being characteristic velocity and length scales, $\nu$ the kinematic viscosity and $N$ the buoyancy frequency). Since a sufficiently large dynamic range (largely) unaffected by stratification and viscosity is required, it is also necessary for the buoyancy Reynolds number $Re_{b} := \epsilon /(\nu N^{2}) \gg 1$ , where $\epsilon$ is the (appropriately volume-averaged) turbulent kinetic energy dissipation rate. This requirement is exacerbated for oceanically relevant flows, as the Prandtl number $Pr := \nu /\kappa = {O}(10)$ in thermally stratified water (where $\kappa$ is the thermal diffusivity), thus leading (potentially) to even finer density field structures. We report here on four forced fully resolved direct numerical simulations of stratified turbulence at various Froude ( $Fr=0.5, 2$ ) and Prandtl ( $Pr=1, 7$ ) numbers forced so that $Re_{b}=50$ , with resolutions up to $30\,240 \times 30\,240 \times 3780$ . We find that, as $Pr$ increases, emergent ‘interfaces’ become finer and their contribution to bulk mixing characteristics decreases at the expense of the small-scale density structures populating the well-mixed ‘layers’. However, extreme mixing events (as quantified by significantly elevated local destruction rates of buoyancy variance $\chi _0$ ) are always preferentially found in the (statically stable) interfaces, irrespective of the value of $Pr$ .

Modified stochastic diffusion particle tracking model driven by skew Brownian motion: Analysis of sediment particle motion in turbulent flow under the effects of ejection and sweep.

Turbulent bursting events have been classified into outward interactions (Q1), ejections (Q2), inward interactions (Q3), and sweeps (Q4) in various studies. Ejections (Q2) and sweeps (Q4) have been identified as significant contributors to time consumption, momentum flux, and sediment flux. Additionally, research has shown that the distribution of these events varies nonuniformly at different bed elevations. Despite extensive investigations into the nonuniform distribution of turbulent bursting events, their impact on sediment transport has been rarely explored. In this work, we developed a modified stochastic diffusion particle tracking model (SD-PTM) driven by skew Brownian motion (SBM) using the stochastic Lagrangian approach to scrutinize sediment particle movement in turbulent flows. The model incorporates turbulent characteristics derived from a direct numerical simulation dataset, allowing for a comprehensive analysis of sediment particle dynamics. Moreover, the proposed model accounts for the nonuniform spatial distribution of ejection and sweep events, as well as the particle movement direction during these events. Numerical simulations of the model were conducted to trace sediment particle trajectories in the streamwise and vertical directions. The analysis of sediment transport involved calculating the variance of particle trajectories to examine anomalous diffusion. The model's performance was evaluated by comparing it with flow velocity and sediment concentration profiles obtained from measurements in previous studies. In conclusion, our study suggests that the motion of sediment particles in turbulent flow can be thoroughly investigated under extreme flow conditions using the modified SD-PTM driven by SBM.

Time-averaged flow characterisations in a bimodal gravel-bed stream and relative role of sediment depositions on near-bed coherent flow structures

Abstract The present study aims to quantify experimentally the relative role of sediment depositions on near-bed flows and turbulence in a gravel-bed stream. Time-averaged velocity was measured over a sand-filled gravel-bed stream with four cases of sediment depositions and compared with those over a gravel-bed stream without sediment depositions. An acoustic Doppler velocimeter was used to measure the instantaneous velocity of flows. The progressive infilling of void spaces in the gravel-bed stream forms distinct bimodal depositions that alter the mean flows characterised by increasing zero-velocity levels and massive damping in the bed shear stresses. The data plots of turbulent intensity depict enhancement of streamwise turbulent intensity in the near-bed flow region with increasing sand depositions. Moreover, the opposite nature of streamwise and vertical turbulent kinetic energy fluxes in the interfacial sublayer leads to slowing down the time-average Reynolds shear stresses at the vicinity of the gravel-bed surface. At the vicinity of the crest, the ejection and sweep events contributed approximately 86 and 56%, respectively, to the total Reynolds shear stress production in the case of gravel bed under clear-water flow conditions. By contrast, the contributions of turbulent sweep events increased over the sand-filled gravel bed at the same location.

Damnatio Memoriae of Serbian Culture: The Institute for the Preservation of Antiquities of Milan Nedić's Government, Precursor of the Republic Institute for the Protection of Cultural Monuments

Currently, the conservation activities in Serbia are conducted by the institutions for the protection of cultural monuments. It is widely accepted that the first institute of the kind was The Republic Institute for the Protection of Cultural Monuments, established in 1947, during the enactment of several heritage laws, which were also presented as the first of their kind. The main contribution for the establishment of the Institute was attributed to its first director, Milorad Panić-Surep.
 During the Second World War, in the spring of 1942, at the initiative of the occupiers, the collaborationist government of Milan Nedić enacted the Regulation on the Preservation of Antiquities. It was the first legal act aimed at protecting cultural heritage in Serbia. On this basis, the Institute for the Protection of Antiquities was established, which actively operated despite the wartime conditions. From its establishment until the founding of the post-war Institute in 1947, there was an uninterrupted continuity, marked by several transformations due to the turbulent post-war events.
 Towards the end of the war, in early 1945, the Institute for the Protection of Antiquities ceased its operations but was not dissolved, it was merged with the Art Museum instead (formerly the Museum of Prince Paul, now the National Museum of Serbia). Thus, both before and after the war, in addition to its regular activities, the Museum also engaged in conservation efforts. The Museum director, Veljko Petrović, insisted that, following the example from the occupation years, an independent institution for conservation activities should be established.
 This was realized in several steps. Firstly, in April, a special committee for creating the Register of Antiquities was formed under the auspices of the Museum, taking over the responsibilities of the former Institute. Subsequently, at the turn of 1945 to 1946, the committee evolved into a separate museum Department for the Protection and Scientific Study of Cultural Monuments, and by mid-1947, it became an independent institution as the Institute for the Protection and Scientific Research of Cultural Monuments. This institution took over all previously created documentation as well as leading experts and in 1960 it changed its name to the Republic Institute for the Protection of Cultural Monuments.
 Following the given facts, the Institute for the Protection of Antiquities from 1942 is a direct precursor to the Republic Institute for the Protection of Cultural Monuments, with a continuous legacy. Driven by ideological reasons, the post-war authorities intended to demonstrate that numerous innovative moves of state administration were initiated during their tenure. Consequently, they endeavored to conceal or minimize initiatives undertaken by previous authorities, particularly the ones associated with the collaborationist government during the occupation period, whose entire activity was presented as contrary to the interests of the people.

Identification of the Optimal Neural Network Architecture for Prediction of Bitcoin Return

Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.

Exploring Volatility Spillover Dynamics between Emerging South Asian Stock Markets and the U.S. Market: Empirical Insights from the M-GARCH-BEKK Framework

Abstract: This study investigates the volatility spillover between emerging South Asian equity markets and developed U.S. market. Specifically, the study examines the entire sample period and three sub-periods: pre-crisis, crisis, and post-Global Financial Crisis (GFC) periods in the emerging markets of Pakistan, India, and Sri Lanka, as well as the U.S. as developed stock market. The study analyses both unidirectional and bidirectional shock and volatility spillover among the markets to determine the direction of risk transmissions. The analysis utilizes a multivariate GARCH-BEKK model. The results indicate that volatility transmissions increased due to the GFC. Furthermore, the South Asian emerging markets exhibit shock and volatility spillover during the crisis period compared to the pre- and post-crisis periods. Additionally, the patterns of risk transmission were time-varying. These findings support the notion that market volatility increases during turbulent events, such as the Global Financial Crisis. The implications of these results are important for policymakers, investors, and researchers interested in international diversification and market efficiency within the studied markets especially during turbulent periods.

Turbulent Events Effects: Socioeconomic Changes in Southern Poland as Captured by the LSED Index

Today’s generations live in uncertain times. The inflation of violent and unpredictable events over the last two decades, such as the economic crisis or COVID-19 pandemic, has affected the functioning of regions and the daily lives of their residents. Therefore, the socioeconomic level has to be monitored. This article fills the research gap regarding the identification of the impact of recent turbulent events on the development of municipalities in southern Poland. The specific goal is to identify trends in socioeconomic changes in times of change and uncertainty from 2006 to 2021 in 450 municipalities in Małopolskie, Śląskie, and Świętokrzyskie Voivodeships. The research defined model and problem areas among the municipalities regarding the level of development. The analyses employ an original synthetic Level of SocioEconomic Development index (LSED) consisting of 18 diagnostic variables. The study mixed qualitative and quantitative approaches and considered the spatial dimension in statistical analyses. We identified general trends related to the ageing population and housing shortage. Moreover, in municipalities dominated by industry, socioeconomic development was generally constant. The opposite is true for municipalities focusing on tourism or agriculture. The conclusions demonstrate that Poland’s European Union membership was the key driver of the socioeconomic development of the regions and the country at large. The rule of law crisis in Poland and ensuing cuts in EU spending could slow the development down. The crisis brought about by the COVID-19 pandemic might be consequential as well.